Overview

Dataset statistics

Number of variables18
Number of observations74
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.2 KiB
Average record size in memory141.7 B

Variable types

Numeric18

Alerts

population is highly correlated with GDP_constant_2010_USD and 5 other fieldsHigh correlation
GDP_constant_2010_USD is highly correlated with population and 7 other fieldsHigh correlation
land_area_km_sq is highly correlated with population and 4 other fieldsHigh correlation
population_% is highly correlated with population and 5 other fieldsHigh correlation
GDP_% is highly correlated with population and 9 other fieldsHigh correlation
land_area_% is highly correlated with population and 4 other fieldsHigh correlation
EF.EFM.OVRL.XD is highly correlated with GDP_constant_2010_USD and 4 other fieldsHigh correlation
EF.EFM.RANK.XD is highly correlated with GDP_% and 3 other fieldsHigh correlation
1.1_ACCESS.ELECTRICITY.TOT is highly correlated with 1.2_ACCESS.ELECTRICITY.RURALHigh correlation
1.2_ACCESS.ELECTRICITY.RURAL is highly correlated with 1.1_ACCESS.ELECTRICITY.TOTHigh correlation
SH.UHC.NOP2.TO is highly correlated with population and 1 other fieldsHigh correlation
CC.EST is highly correlated with GE.EST and 2 other fieldsHigh correlation
GE.EST is highly correlated with GDP_constant_2010_USD and 6 other fieldsHigh correlation
RQ.EST is highly correlated with GDP_% and 5 other fieldsHigh correlation
VA.EST is highly correlated with GDP_constant_2010_USD and 4 other fieldsHigh correlation
population is highly correlated with GDP_constant_2010_USD and 4 other fieldsHigh correlation
GDP_constant_2010_USD is highly correlated with population and 6 other fieldsHigh correlation
land_area_km_sq is highly correlated with land_area_%High correlation
population_% is highly correlated with population and 4 other fieldsHigh correlation
GDP_% is highly correlated with population and 7 other fieldsHigh correlation
land_area_% is highly correlated with land_area_km_sqHigh correlation
EF.EFM.OVRL.XD is highly correlated with population and 6 other fieldsHigh correlation
EF.EFM.RANK.XD is highly correlated with EF.EFM.OVRL.XDHigh correlation
1.1_ACCESS.ELECTRICITY.TOT is highly correlated with 1.2_ACCESS.ELECTRICITY.RURALHigh correlation
1.2_ACCESS.ELECTRICITY.RURAL is highly correlated with 1.1_ACCESS.ELECTRICITY.TOTHigh correlation
SH.UHC.NOP1.CG is highly correlated with SH.UHC.NOP2.TOHigh correlation
SH.UHC.NOP2.TO is highly correlated with population and 4 other fieldsHigh correlation
CC.EST is highly correlated with GDP_constant_2010_USD and 5 other fieldsHigh correlation
GE.EST is highly correlated with GDP_constant_2010_USD and 5 other fieldsHigh correlation
RQ.EST is highly correlated with CC.EST and 2 other fieldsHigh correlation
VA.EST is highly correlated with GDP_% and 3 other fieldsHigh correlation
population is highly correlated with land_area_km_sq and 1 other fieldsHigh correlation
GDP_constant_2010_USD is highly correlated with GDP_%High correlation
land_area_km_sq is highly correlated with population and 2 other fieldsHigh correlation
population_% is highly correlated with population and 2 other fieldsHigh correlation
GDP_% is highly correlated with GDP_constant_2010_USDHigh correlation
land_area_% is highly correlated with land_area_km_sq and 1 other fieldsHigh correlation
EF.EFM.OVRL.XD is highly correlated with EF.EFM.RANK.XDHigh correlation
EF.EFM.RANK.XD is highly correlated with EF.EFM.OVRL.XDHigh correlation
1.1_ACCESS.ELECTRICITY.TOT is highly correlated with 1.2_ACCESS.ELECTRICITY.RURALHigh correlation
1.2_ACCESS.ELECTRICITY.RURAL is highly correlated with 1.1_ACCESS.ELECTRICITY.TOTHigh correlation
CC.EST is highly correlated with GE.EST and 1 other fieldsHigh correlation
GE.EST is highly correlated with CC.EST and 2 other fieldsHigh correlation
RQ.EST is highly correlated with CC.EST and 2 other fieldsHigh correlation
VA.EST is highly correlated with GE.EST and 1 other fieldsHigh correlation
population is highly correlated with GDP_constant_2010_USD and 8 other fieldsHigh correlation
GDP_constant_2010_USD is highly correlated with population and 11 other fieldsHigh correlation
land_area_km_sq is highly correlated with population and 11 other fieldsHigh correlation
population_% is highly correlated with population and 10 other fieldsHigh correlation
GDP_% is highly correlated with population and 11 other fieldsHigh correlation
land_area_% is highly correlated with population and 8 other fieldsHigh correlation
EF.EFM.OVRL.XD is highly correlated with population and 11 other fieldsHigh correlation
EF.EFM.RANK.XD is highly correlated with population_% and 5 other fieldsHigh correlation
1.1_ACCESS.ELECTRICITY.TOT is highly correlated with 1.2_ACCESS.ELECTRICITY.RURAL and 1 other fieldsHigh correlation
1.2_ACCESS.ELECTRICITY.RURAL is highly correlated with 1.1_ACCESS.ELECTRICITY.TOTHigh correlation
SH.UHC.NOP1.CG is highly correlated with GDP_constant_2010_USD and 4 other fieldsHigh correlation
SH.UHC.NOP2.TO is highly correlated with population and 9 other fieldsHigh correlation
SH.UHC.OOPC.25.ZS is highly correlated with population and 8 other fieldsHigh correlation
CC.EST is highly correlated with GDP_constant_2010_USD and 7 other fieldsHigh correlation
GE.EST is highly correlated with population and 12 other fieldsHigh correlation
PV.EST is highly correlated with CC.EST and 3 other fieldsHigh correlation
RQ.EST is highly correlated with GDP_constant_2010_USD and 8 other fieldsHigh correlation
VA.EST is highly correlated with land_area_km_sq and 7 other fieldsHigh correlation
population has unique values Unique
GDP_constant_2010_USD has unique values Unique
population_% has unique values Unique
GDP_% has unique values Unique
land_area_% has unique values Unique
1.1_ACCESS.ELECTRICITY.TOT has unique values Unique
CC.EST has unique values Unique
GE.EST has unique values Unique
PV.EST has unique values Unique
RQ.EST has unique values Unique
VA.EST has unique values Unique
EF.EFM.OVRL.XD has 2 (2.7%) zeros Zeros
SH.UHC.NOP1.CG has 4 (5.4%) zeros Zeros
SH.UHC.NOP2.TO has 2 (2.7%) zeros Zeros

Reproduction

Analysis started2023-01-06 11:09:07.373130
Analysis finished2023-01-06 11:09:24.816950
Duration17.44 seconds
Software versionpandas-profiling v3.2.0
Download configurationconfig.json

Variables

population
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32006334.08
Minimum1756817
Maximum120828307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2023-01-06T12:09:24.847676image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1756817
5-th percentile3738798.75
Q114524493
median26095787.5
Q345420361
95-th percentile76839721.6
Maximum120828307
Range119071490
Interquartile range (IQR)30895868

Descriptive statistics

Standard deviation25412519.64
Coefficient of variation (CV)0.7939840776
Kurtosis1.294910043
Mean32006334.08
Median Absolute Deviation (MAD)16400477.5
Skewness1.18969334
Sum2368468722
Variance6.457961547 × 1014
MonotonicityNot monotonic
2023-01-06T12:09:24.917549image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
404039581
 
1.4%
1208283071
 
1.4%
87634001
 
1.4%
210823831
 
1.4%
152742341
 
1.4%
35162681
 
1.4%
831848921
 
1.4%
248540341
 
1.4%
301792851
 
1.4%
403823891
 
1.4%
Other values (64)64
86.5%
ValueCountFrequency (%)
17568171
1.4%
20348191
1.4%
31682151
1.4%
35162681
1.4%
38586231
1.4%
46161001
1.4%
48984001
1.4%
53187001
1.4%
60411121
1.4%
71990771
1.4%
ValueCountFrequency (%)
1208283071
1.4%
1017196731
1.4%
831848921
1.4%
809538811
1.4%
746244051
1.4%
745698671
1.4%
704400321
1.4%
670078551
1.4%
665457601
1.4%
665373311
1.4%

GDP_constant_2010_USD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.50191943 × 1011
Minimum1444065193
Maximum1.13759 × 1012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2023-01-06T12:09:24.986666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1444065193
5-th percentile3094804228
Q11.635696167 × 1010
median4.199411927 × 1010
Q31.86027 × 1011
95-th percentile6.53729 × 1011
Maximum1.13759 × 1012
Range1.136145935 × 1012
Interquartile range (IQR)1.696700383 × 1011

Descriptive statistics

Standard deviation2.360883656 × 1011
Coefficient of variation (CV)1.571910988
Kurtosis5.420530109
Mean1.50191943 × 1011
Median Absolute Deviation (MAD)3.522815374 × 1010
Skewness2.307960669
Sum1.111420378 × 1013
Variance5.573771635 × 1022
MonotonicityNot monotonic
2023-01-06T12:09:25.052003image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.92491 × 10111
 
1.4%
1.13759 × 10121
 
1.4%
4.611389902 × 10101
 
1.4%
2.846019905 × 10101
 
1.4%
1.77705236 × 10101
 
1.4%
2.691026184 × 10101
 
1.4%
2.444415613 × 10101
 
1.4%
2.98713459 × 10101
 
1.4%
7.068730463 × 10101
 
1.4%
4.08877 × 10111
 
1.4%
Other values (64)64
86.5%
ValueCountFrequency (%)
14440651931
1.4%
19100000001
1.4%
21455319091
1.4%
28900000001
1.4%
32050834281
1.4%
34098808911
1.4%
35543622041
1.4%
46350732161
1.4%
46819389611
1.4%
51038730201
1.4%
ValueCountFrequency (%)
1.13759 × 10121
1.4%
8.98741 × 10111
1.4%
8.80872 × 10111
1.4%
7.46614 × 10111
1.4%
6.03714 × 10111
1.4%
5.20931 × 10111
1.4%
4.59411 × 10111
1.4%
4.44453 × 10111
1.4%
4.32657 × 10111
1.4%
4.12337 × 10111
1.4%

land_area_km_sq
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct38
Distinct (%)51.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean725908.5405
Minimum25430
Maximum2736690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size424.0 B
2023-01-06T12:09:25.114638image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum25430
5-th percentile72642.5
Q1183622.5
median510890
Q3882050
95-th percentile2699700
Maximum2736690
Range2711260
Interquartile range (IQR)698427.5

Descriptive statistics

Standard deviation756601.7991
Coefficient of variation (CV)1.042282542
Kurtosis2.217769825
Mean725908.5405
Median Absolute Deviation (MAD)363230
Skewness1.729203627
Sum53717232
Variance5.724462825 × 1011
MonotonicityNot monotonic
2023-01-06T12:09:25.177315image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
4254005
 
6.8%
7696305
 
6.8%
26997004
 
5.4%
10000004
 
5.4%
1204104
 
5.4%
11095003
 
4.1%
5691403
 
4.1%
8820503
 
4.1%
27366903
 
4.1%
4727103
 
4.1%
Other values (28)37
50.0%
ValueCountFrequency (%)
254301
1.4%
256801
1.4%
284701
1.4%
694901
1.4%
743401
1.4%
826051
1.4%
826271
1.4%
826581
1.4%
826721
1.4%
874601
1.4%
ValueCountFrequency (%)
27366903
4.1%
26997004
5.4%
19439502
 
2.7%
11095003
4.1%
10000004
5.4%
9954501
 
1.4%
8820503
4.1%
7863802
 
2.7%
7696305
6.8%
6528602
 
2.7%

population_%
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.004930800882
Minimum0.0002468588638
Maximum0.01697817051
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2023-01-06T12:09:25.241454image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0002468588638
5-th percentile0.0005815902497
Q10.002185543028
median0.004043668126
Q30.006618529385
95-th percentile0.01174641412
Maximum0.01697817051
Range0.01673131165
Interquartile range (IQR)0.004432986357

Descriptive statistics

Standard deviation0.003879795795
Coefficient of variation (CV)0.7868490105
Kurtosis0.8679450042
Mean0.004930800882
Median Absolute Deviation (MAD)0.002438398463
Skewness1.117698243
Sum0.3648792653
Variance1.505281541 × 10-5
MonotonicityNot monotonic
2023-01-06T12:09:25.307765image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0066288581571
 
1.4%
0.016978170511
 
1.4%
0.0012981269581
 
1.4%
0.0029623877241
 
1.4%
0.0025059606941
 
1.4%
0.00052086659091
 
1.4%
0.012322220921
 
1.4%
0.003492360671
 
1.4%
0.0046970121621
 
1.4%
0.0059818640931
 
1.4%
Other values (64)64
86.5%
ValueCountFrequency (%)
0.00024685886381
1.4%
0.00033384171241
1.4%
0.00052086659091
1.4%
0.00054638977371
1.4%
0.00060054435221
1.4%
0.0007878617721
1.4%
0.00079609175331
1.4%
0.00080365390931
1.4%
0.001011577171
1.4%
0.001041849061
1.4%
ValueCountFrequency (%)
0.016978170511
1.4%
0.016688594821
1.4%
0.012322220921
1.4%
0.011991740111
1.4%
0.011614315511
1.4%
0.010916418871
1.4%
0.010478173111
1.4%
0.010434318241
1.4%
0.010428898831
1.4%
0.010375463511
1.4%

GDP_%
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.002532031423
Minimum3.296399238 × 10-5
Maximum0.01759334586
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2023-01-06T12:09:25.377500image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3.296399238 × 10-5
5-th percentile5.970047728 × 10-5
Q10.000279094087
median0.0006008625473
Q30.002918849849
95-th percentile0.01108734678
Maximum0.01759334586
Range0.01756038187
Interquartile range (IQR)0.002639755762

Descriptive statistics

Standard deviation0.00391181603
Coefficient of variation (CV)1.544931865
Kurtosis4.50349052
Mean0.002532031423
Median Absolute Deviation (MAD)0.0005222091005
Skewness2.182893716
Sum0.1873703253
Variance1.530230465 × 10-5
MonotonicityNot monotonic
2023-01-06T12:09:25.536253image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0038445548711
 
1.4%
0.016276927271
 
1.4%
0.00071800689571
 
1.4%
0.0004072157721
 
1.4%
0.0003549244021
 
1.4%
0.00041900064781
 
1.4%
0.00038060266071
 
1.4%
0.00042740682031
 
1.4%
0.0012642912921
 
1.4%
0.0063663344831
 
1.4%
Other values (64)64
86.5%
ValueCountFrequency (%)
3.296399238 × 10-51
1.4%
3.837432088 × 10-51
1.4%
4.359998824 × 10-51
1.4%
5.168964716 × 10-51
1.4%
6.401400119 × 10-51
1.4%
7.289915905 × 10-51
1.4%
7.783809778 × 10-51
1.4%
7.946879576 × 10-51
1.4%
8.113620435 × 10-51
1.4%
9.257468302 × 10-51
1.4%
ValueCountFrequency (%)
0.017593345861
1.4%
0.016276927271
1.4%
0.012859414991
1.4%
0.011624998361
1.4%
0.010797842091
1.4%
0.01048707551
1.4%
0.010404371181
1.4%
0.0064319798361
1.4%
0.0064202076951
1.4%
0.0063663344831
1.4%

land_area_%
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.005578633011
Minimum0.0001959191969
Maximum0.02117815218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2023-01-06T12:09:25.605060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0001959191969
5-th percentile0.0005594918003
Q10.001406732764
median0.003963058618
Q30.006790418782
95-th percentile0.02078773032
Maximum0.02117815218
Range0.02098223298
Interquartile range (IQR)0.005383686018

Descriptive statistics

Standard deviation0.005802548584
Coefficient of variation (CV)1.040138072
Kurtosis2.200012691
Mean0.005578633011
Median Absolute Deviation (MAD)0.002753428818
Skewness1.722964859
Sum0.4128188428
Variance3.366957007 × 10-5
MonotonicityNot monotonic
2023-01-06T12:09:25.670833image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0086271738111
 
1.4%
0.014731255281
 
1.4%
0.00063941739111
 
1.4%
0.0035821969111
 
1.4%
0.0036756659141
 
1.4%
0.00057528760391
 
1.4%
0.0077386010751
 
1.4%
0.00091246711531
 
1.4%
0.0034538449731
 
1.4%
0.021178152181
 
1.4%
Other values (64)64
86.5%
ValueCountFrequency (%)
0.00019591919691
1.4%
0.00019773684541
1.4%
0.00021720481061
1.4%
0.00053015673651
1.4%
0.00057528760391
1.4%
0.00062638241691
1.4%
0.00063941739111
1.4%
0.00063978550661
1.4%
0.00064231427911
1.4%
0.00066277197821
1.4%
ValueCountFrequency (%)
0.021178152181
1.4%
0.020992141631
1.4%
0.020891901321
1.4%
0.020878898251
1.4%
0.020738639891
1.4%
0.020596692211
1.4%
0.020458329631
1.4%
0.015115632741
1.4%
0.014731255281
1.4%
0.0086271738111
1.4%

EF.EFM.OVRL.XD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct73
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6245125135
Minimum0
Maximum2.5056
Zeros2
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size720.0 B
2023-01-06T12:09:25.734198image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.008389915
Q10.1099025
median0.392215
Q30.953925
95-th percentile2.194775
Maximum2.5056
Range2.5056
Interquartile range (IQR)0.8440225

Descriptive statistics

Standard deviation0.6800190707
Coefficient of variation (CV)1.088879816
Kurtosis0.687452854
Mean0.6245125135
Median Absolute Deviation (MAD)0.3116335
Skewness1.279064117
Sum46.213926
Variance0.4624259365
MonotonicityNot monotonic
2023-01-06T12:09:25.800625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02
 
2.7%
0.536391
 
1.4%
0.568181
 
1.4%
2.22831
 
1.4%
0.0153441
 
1.4%
0.0496541
 
1.4%
0.0285081
 
1.4%
2.18621
 
1.4%
0.139171
 
1.4%
0.65311
 
1.4%
Other values (63)63
85.1%
ValueCountFrequency (%)
02
2.7%
0.00675971
1.4%
0.00693061
1.4%
0.00917571
1.4%
0.0153441
1.4%
0.0178571
1.4%
0.0243651
1.4%
0.0285081
1.4%
0.0318721
1.4%
0.0362421
1.4%
ValueCountFrequency (%)
2.50561
1.4%
2.42691
1.4%
2.22831
1.4%
2.21071
1.4%
2.18621
1.4%
1.90181
1.4%
1.89121
1.4%
1.7281
1.4%
1.71491
1.4%
1.55431
1.4%

EF.EFM.RANK.XD
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct56
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.06756757
Minimum18
Maximum144
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2023-01-06T12:09:25.865794image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile21.65
Q147.5
median73.5
Q3102
95-th percentile130.35
Maximum144
Range126
Interquartile range (IQR)54.5

Descriptive statistics

Standard deviation34.89213558
Coefficient of variation (CV)0.458699242
Kurtosis-1.020241182
Mean76.06756757
Median Absolute Deviation (MAD)27.5
Skewness0.04190017397
Sum5629
Variance1217.461126
MonotonicityNot monotonic
2023-01-06T12:09:25.934163image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
683
 
4.1%
1293
 
4.1%
922
 
2.7%
182
 
2.7%
892
 
2.7%
702
 
2.7%
1312
 
2.7%
982
 
2.7%
232
 
2.7%
1022
 
2.7%
Other values (46)52
70.3%
ValueCountFrequency (%)
182
2.7%
191
1.4%
211
1.4%
221
1.4%
232
2.7%
261
1.4%
301
1.4%
311
1.4%
321
1.4%
361
1.4%
ValueCountFrequency (%)
1441
 
1.4%
1371
 
1.4%
1312
2.7%
1301
 
1.4%
1293
4.1%
1251
 
1.4%
1221
 
1.4%
1181
 
1.4%
1141
 
1.4%
1131
 
1.4%

1.1_ACCESS.ELECTRICITY.TOT
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.67502608
Minimum2.318697929
Maximum99.99990845
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2023-01-06T12:09:26.001573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum2.318697929
5-th percentile9.515150166
Q156.76608181
median97.119376
Q399.40689087
95-th percentile99.88461037
Maximum99.99990845
Range97.68121052
Interquartile range (IQR)42.64080906

Descriptive statistics

Standard deviation33.52316035
Coefficient of variation (CV)0.4372109416
Kurtosis-0.2558916299
Mean76.67502608
Median Absolute Deviation (MAD)2.721855545
Skewness-1.182942946
Sum5673.95193
Variance1123.80228
MonotonicityNot monotonic
2023-01-06T12:09:26.067051image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.21
 
1.4%
99.1116371
 
1.4%
99.741806031
 
1.4%
55.249626161
 
1.4%
411
 
1.4%
86.685844421
 
1.4%
21.763442991
 
1.4%
32.066120151
 
1.4%
78.21
 
1.4%
97.934127811
 
1.4%
Other values (64)64
86.5%
ValueCountFrequency (%)
2.3186979291
1.4%
2.3244094851
1.4%
2.7807688711
1.4%
6.6950588231
1.4%
11.033660891
1.4%
11.64911271
1.4%
12.71
1.4%
15.459075931
1.4%
18.294923781
1.4%
21.763442991
1.4%
ValueCountFrequency (%)
99.999908451
1.4%
99.999710081
1.4%
99.985656741
1.4%
99.90295411
1.4%
99.874732971
1.4%
99.867491171
1.4%
99.814971921
1.4%
99.81
1.4%
99.782707211
1.4%
99.741806031
1.4%

1.2_ACCESS.ELECTRICITY.RURAL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION

Distinct70
Distinct (%)94.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.39097633
Minimum0.4
Maximum99.99989708
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2023-01-06T12:09:26.133075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile10.89371013
Q154.425
median93.6426151
Q398.97021259
95-th percentile99.78683329
Maximum99.99989708
Range99.59989708
Interquartile range (IQR)44.54521259

Descriptive statistics

Standard deviation33.34088624
Coefficient of variation (CV)0.4422397462
Kurtosis-0.386971062
Mean75.39097633
Median Absolute Deviation (MAD)5.986558532
Skewness-1.139504667
Sum5578.932248
Variance1111.614695
MonotonicityNot monotonic
2023-01-06T12:09:26.196695image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
95.009054374
 
5.4%
99.32
 
2.7%
83.81
 
1.4%
9.4908196391
 
1.4%
97.1940361
 
1.4%
97.720743941
 
1.4%
99.587368881
 
1.4%
18.828431571
 
1.4%
211
 
1.4%
66.202675241
 
1.4%
Other values (60)60
81.1%
ValueCountFrequency (%)
0.41
1.4%
3.3285146411
1.4%
3.4020885291
1.4%
9.4908196391
1.4%
11.64911271
1.4%
14.608634461
1.4%
15.51
1.4%
18.294923781
1.4%
18.389883161
1.4%
18.828431571
1.4%
ValueCountFrequency (%)
99.999897081
1.4%
99.999856541
1.4%
99.98692151
1.4%
99.847639691
1.4%
99.754091381
1.4%
99.73306641
1.4%
99.660654841
1.4%
99.659153071
1.4%
99.599194191
1.4%
99.590629651
1.4%

SH.UHC.NOP1.CG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct18
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0006841081081
Minimum0
Maximum0.0041634
Zeros4
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size720.0 B
2023-01-06T12:09:26.256350image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5405 × 10-5
Q10.0005343
median0.0005343
Q30.0005343
95-th percentile0.002177595
Maximum0.0041634
Range0.0041634
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0007886012972
Coefficient of variation (CV)1.152743679
Kurtosis11.88768073
Mean0.0006841081081
Median Absolute Deviation (MAD)0
Skewness3.423608876
Sum0.050624
Variance6.21892006 × 10-7
MonotonicityNot monotonic
2023-01-06T12:09:26.303380image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.000534354
73.0%
04
 
5.4%
0.00387521
 
1.4%
0.00014621
 
1.4%
0.00034931
 
1.4%
0.00081361
 
1.4%
9.05 × 10-51
 
1.4%
0.00015531
 
1.4%
0.00416341
 
1.4%
0.00102741
 
1.4%
Other values (8)8
 
10.8%
ValueCountFrequency (%)
04
 
5.4%
2.37 × 10-51
 
1.4%
4.15 × 10-51
 
1.4%
9.05 × 10-51
 
1.4%
0.00014621
 
1.4%
0.00015531
 
1.4%
0.00018411
 
1.4%
0.00034931
 
1.4%
0.0004861
 
1.4%
0.000534354
73.0%
ValueCountFrequency (%)
0.00416341
 
1.4%
0.00398431
 
1.4%
0.00387521
 
1.4%
0.00268181
 
1.4%
0.00190611
 
1.4%
0.00184341
 
1.4%
0.00102741
 
1.4%
0.00081361
 
1.4%
0.000534354
73.0%
0.0004861
 
1.4%

SH.UHC.NOP2.TO
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126175.6757
Minimum0
Maximum1484000
Zeros2
Zeros (%)2.7%
Negative0
Negative (%)0.0%
Memory size720.0 B
2023-01-06T12:09:26.354083image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14100
Q169500
median69500
Q369500
95-th percentile501300
Maximum1484000
Range1484000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation210371.531
Coefficient of variation (CV)1.667290703
Kurtosis25.26512252
Mean126175.6757
Median Absolute Deviation (MAD)0
Skewness4.626652498
Sum9337000
Variance4.425618104 × 1010
MonotonicityNot monotonic
2023-01-06T12:09:26.404643image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
6950054
73.0%
02
 
2.7%
14840001
 
1.4%
540001
 
1.4%
3420001
 
1.4%
8340001
 
1.4%
2150001
 
1.4%
10001
 
1.4%
2580001
 
1.4%
50001
 
1.4%
Other values (10)10
 
13.5%
ValueCountFrequency (%)
02
 
2.7%
10001
 
1.4%
50001
 
1.4%
190001
 
1.4%
300001
 
1.4%
350001
 
1.4%
470001
 
1.4%
540001
 
1.4%
6950054
73.0%
1490001
 
1.4%
ValueCountFrequency (%)
14840001
1.4%
8340001
1.4%
5590001
1.4%
5520001
1.4%
4740001
1.4%
3420001
1.4%
3350001
1.4%
2580001
1.4%
2150001
1.4%
1910001
1.4%

SH.UHC.OOPC.25.ZS
Real number (ℝ≥0)

HIGH CORRELATION

Distinct21
Distinct (%)28.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.308862675
Minimum0.0924415
Maximum3.109187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size720.0 B
2023-01-06T12:09:26.460327image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.0924415
5-th percentile0.31211069
Q11.439833014
median1.439833014
Q31.439833014
95-th percentile1.439833014
Maximum3.109187
Range3.0167455
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.4521915519
Coefficient of variation (CV)0.3454843357
Kurtosis4.416208475
Mean1.308862675
Median Absolute Deviation (MAD)0
Skewness-0.1732015388
Sum96.85583795
Variance0.2044771996
MonotonicityNot monotonic
2023-01-06T12:09:26.507936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1.43983301454
73.0%
0.29760881
 
1.4%
2.441681
 
1.4%
1.2463651
 
1.4%
1.2588251
 
1.4%
1.911831
 
1.4%
0.68036371
 
1.4%
0.22270781
 
1.4%
0.1763311
 
1.4%
1.0394161
 
1.4%
Other values (11)11
 
14.9%
ValueCountFrequency (%)
0.09244151
1.4%
0.1763311
1.4%
0.22270781
1.4%
0.29760881
1.4%
0.31991941
1.4%
0.42288361
1.4%
0.51960861
1.4%
0.53411361
1.4%
0.68036371
1.4%
0.73500211
1.4%
ValueCountFrequency (%)
3.1091871
 
1.4%
2.441681
 
1.4%
1.911831
 
1.4%
1.43983301454
73.0%
1.2588251
 
1.4%
1.2463651
 
1.4%
1.1458741
 
1.4%
1.0394161
 
1.4%
1.0383341
 
1.4%
1.0265071
 
1.4%

CC.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.8011090119
Minimum-1.672095537
Maximum0.1613447815
Zeros0
Zeros (%)0.0%
Negative72
Negative (%)97.3%
Memory size720.0 B
2023-01-06T12:09:26.568482image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-1.672095537
5-th percentile-1.401429707
Q1-1.148523033
median-0.9140115678
Q3-0.426721096
95-th percentile-0.09168723933
Maximum0.1613447815
Range1.833440319
Interquartile range (IQR)0.7218019366

Descriptive statistics

Standard deviation0.4559729941
Coefficient of variation (CV)-0.5691772123
Kurtosis-1.006707244
Mean-0.8011090119
Median Absolute Deviation (MAD)0.3513569534
Skewness0.3181497308
Sum-59.28206688
Variance0.2079113714
MonotonicityNot monotonic
2023-01-06T12:09:26.634580image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.4062625171
 
1.4%
-0.48452758791
 
1.4%
-1.1566542391
 
1.4%
-1.272439481
 
1.4%
-1.325292111
 
1.4%
-0.10128635911
 
1.4%
-0.66639977691
 
1.4%
-1.2870285511
 
1.4%
-0.24378278851
 
1.4%
-0.43487501141
 
1.4%
Other values (64)64
86.5%
ValueCountFrequency (%)
-1.6720955371
1.4%
-1.5272641181
1.4%
-1.5184663531
1.4%
-1.4303728341
1.4%
-1.3858449461
1.4%
-1.325292111
1.4%
-1.3106800321
1.4%
-1.3029065131
1.4%
-1.2923827171
1.4%
-1.2870285511
1.4%
ValueCountFrequency (%)
0.16134478151
1.4%
0.11532557011
1.4%
-0.033657956871
1.4%
-0.073860302571
1.4%
-0.10128635911
1.4%
-0.10131715241
1.4%
-0.10692699251
1.4%
-0.14807352421
1.4%
-0.14811967311
1.4%
-0.19190439581
1.4%

GE.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.651356256
Minimum-2.025118113
Maximum0.4325874448
Zeros0
Zeros (%)0.0%
Negative64
Negative (%)86.5%
Memory size720.0 B
2023-01-06T12:09:26.800996image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.025118113
5-th percentile-1.692307079
Q1-0.9606828392
median-0.727253139
Q3-0.1847485378
95-th percentile0.2120291531
Maximum0.4325874448
Range2.457705557
Interquartile range (IQR)0.7759343013

Descriptive statistics

Standard deviation0.5588480151
Coefficient of variation (CV)-0.8579759692
Kurtosis-0.2712013221
Mean-0.651356256
Median Absolute Deviation (MAD)0.3396545053
Skewness-0.196178623
Sum-48.20036294
Variance0.3123111039
MonotonicityNot monotonic
2023-01-06T12:09:26.867131image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.40307581421
 
1.4%
0.30096364021
 
1.4%
-0.78441536431
 
1.4%
-0.90253967051
 
1.4%
-0.82329106331
 
1.4%
0.12289821361
 
1.4%
-0.44498431681
 
1.4%
-1.9084531071
 
1.4%
-0.20015557111
 
1.4%
-0.12461641431
 
1.4%
Other values (64)64
86.5%
ValueCountFrequency (%)
-2.0251181131
1.4%
-1.9084531071
1.4%
-1.7919441461
1.4%
-1.7478058341
1.4%
-1.6624231341
1.4%
-1.5334354641
1.4%
-1.3764188291
1.4%
-1.2075936791
1.4%
-1.2065011261
1.4%
-1.200643421
1.4%
ValueCountFrequency (%)
0.43258744481
1.4%
0.30096364021
1.4%
0.29018172621
1.4%
0.27618193631
1.4%
0.17748534681
1.4%
0.16942001881
1.4%
0.16569004951
1.4%
0.12289821361
1.4%
0.026065599171
1.4%
0.00023364480881
1.4%

PV.EST
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.68422498
Minimum-2.69136095
Maximum0.6266384721
Zeros0
Zeros (%)0.0%
Negative65
Negative (%)87.8%
Memory size720.0 B
2023-01-06T12:09:26.931479image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.69136095
5-th percentile-1.77459023
Q1-1.217175573
median-0.6012672484
Q3-0.2024248987
95-th percentile0.2496014692
Maximum0.6266384721
Range3.317999423
Interquartile range (IQR)1.014750674

Descriptive statistics

Standard deviation0.6701802611
Coefficient of variation (CV)-0.9794735368
Kurtosis0.3751092578
Mean-0.68422498
Median Absolute Deviation (MAD)0.467003867
Skewness-0.5764027759
Sum-50.63264852
Variance0.4491415824
MonotonicityNot monotonic
2023-01-06T12:09:26.992191image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-1.6089607481
 
1.4%
-0.66245418791
 
1.4%
-0.33034998181
 
1.4%
-0.58851742741
 
1.4%
-0.56401580571
 
1.4%
-0.09778014571
 
1.4%
-1.7321206331
 
1.4%
-0.012876378371
 
1.4%
-0.31266871091
 
1.4%
-0.087755687531
 
1.4%
Other values (64)64
86.5%
ValueCountFrequency (%)
-2.691360951
1.4%
-2.4185614591
1.4%
-2.116748811
1.4%
-1.8534623381
1.4%
-1.7321206331
1.4%
-1.6089607481
1.4%
-1.5705785751
1.4%
-1.4555166961
1.4%
-1.4018104081
1.4%
-1.3936153651
1.4%
ValueCountFrequency (%)
0.62663847211
1.4%
0.52441847321
1.4%
0.45495870711
1.4%
0.29329141971
1.4%
0.22607611121
1.4%
0.11137688161
1.4%
0.10304054621
1.4%
0.090090341871
1.4%
0.042514394971
1.4%
-0.012876378371
1.4%

RQ.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.6560014203
Minimum-2.526690006
Maximum0.7051492929
Zeros0
Zeros (%)0.0%
Negative60
Negative (%)81.1%
Memory size720.0 B
2023-01-06T12:09:27.056660image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.526690006
5-th percentile-2.160910106
Q1-1.097035021
median-0.5181337595
Q3-0.2097485922
95-th percentile0.4300266311
Maximum0.7051492929
Range3.231839299
Interquartile range (IQR)0.8872864284

Descriptive statistics

Standard deviation0.7420070107
Coefficient of variation (CV)-1.131105799
Kurtosis-0.2596349008
Mean-0.6560014203
Median Absolute Deviation (MAD)0.4963677032
Skewness-0.4973314484
Sum-48.5441051
Variance0.550574404
MonotonicityNot monotonic
2023-01-06T12:09:27.117719image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.091514855621
 
1.4%
0.41874319311
 
1.4%
-0.36775231361
 
1.4%
-0.93112206461
 
1.4%
-0.82024645811
 
1.4%
0.70514929291
 
1.4%
-0.89913201331
 
1.4%
-2.5266900061
 
1.4%
-0.24466146531
 
1.4%
-0.7026294471
 
1.4%
Other values (64)64
86.5%
ValueCountFrequency (%)
-2.5266900061
1.4%
-2.2713718411
1.4%
-2.2429056171
1.4%
-2.1858844761
1.4%
-2.1474623681
1.4%
-1.7767726181
1.4%
-1.6414649491
1.4%
-1.6071668861
1.4%
-1.5910269021
1.4%
-1.5811901091
1.4%
ValueCountFrequency (%)
0.70514929291
1.4%
0.51806741951
1.4%
0.48479375241
1.4%
0.45098158721
1.4%
0.41874319311
1.4%
0.32659468051
1.4%
0.27238997821
1.4%
0.26933422681
1.4%
0.24581454691
1.4%
0.22049419581
1.4%

VA.EST
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
UNIQUE

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.7825259756
Minimum-2.240076303
Maximum0.6006909609
Zeros0
Zeros (%)0.0%
Negative63
Negative (%)85.1%
Memory size720.0 B
2023-01-06T12:09:27.186713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-2.240076303
5-th percentile-2.079443872
Q1-1.253632396
median-0.8079130352
Q3-0.2366142794
95-th percentile0.3175000191
Maximum0.6006909609
Range2.840767264
Interquartile range (IQR)1.017018117

Descriptive statistics

Standard deviation0.7158771697
Coefficient of variation (CV)-0.9148286346
Kurtosis-0.695062406
Mean-0.7825259756
Median Absolute Deviation (MAD)0.5227392912
Skewness-0.1939489166
Sum-57.90692219
Variance0.5124801221
MonotonicityNot monotonic
2023-01-06T12:09:27.256464image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.43814095851
 
1.4%
0.11274534461
 
1.4%
-1.3251320121
 
1.4%
-1.0215809351
 
1.4%
-1.0604069231
 
1.4%
0.60069096091
 
1.4%
-1.307212711
 
1.4%
-2.2400763031
 
1.4%
-0.50827461481
 
1.4%
0.35889592771
 
1.4%
Other values (64)64
86.5%
ValueCountFrequency (%)
-2.2400763031
1.4%
-2.226342441
1.4%
-2.1168231961
1.4%
-2.1001427171
1.4%
-2.068298341
1.4%
-2.0335488321
1.4%
-1.9070141321
1.4%
-1.8333867791
1.4%
-1.780859471
1.4%
-1.6003704071
1.4%
ValueCountFrequency (%)
0.60069096091
1.4%
0.46780294181
1.4%
0.39190337061
1.4%
0.35889592771
1.4%
0.29520991441
1.4%
0.25845000151
1.4%
0.19894349581
1.4%
0.11274534461
1.4%
0.090666100381
1.4%
0.013284294871
1.4%

Interactions

2023-01-06T12:09:23.650618image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:07.587303image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:08.592490image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:09.493667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:10.507229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:11.421720image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:12.446752image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:13.391592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:14.263202image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:15.276306image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:16.141127image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:17.143626image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:18.106000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:19.012661image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:19.995662image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:20.898783image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:21.843835image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:22.798710image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:23.696932image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:07.637934image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:08.663860image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:09.541573image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:10.560610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:11.475552image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:12.492700image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:13.441928image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:14.311760image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:15.323230image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:16.194379image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:17.191847image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:18.156557image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:19.062964image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:20.047064image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:21.042713image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:21.892377image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:22.848080image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:23.741381image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:07.687086image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:08.727041image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:09.591704image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:10.614685image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:11.636948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:12.537867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:13.491189image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:14.373594image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:15.371566image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:16.362484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:17.237124image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:18.204405image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:19.112133image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:20.096453image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:21.089210image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:21.939326image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:22.896637image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:23.787439image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:07.737843image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:08.775219image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:09.639709image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:10.666962image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:11.686948image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:12.583096image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:13.538518image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:14.423302image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:15.419313image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:16.410314image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:17.283738image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:18.253592image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:19.164142image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:20.147707image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:21.134842image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:21.992226image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:22.944229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:23.835942image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:07.791749image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:08.825919image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:09.692941image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:10.720101image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:11.739050image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:12.632263image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:13.590627image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:14.476094image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:15.471193image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:16.462077image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:17.332807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:18.316009image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:19.217502image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:20.198859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:21.183841image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:22.042809image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:22.994240image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:23.885524image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:07.845426image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:08.875255image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:09.746106image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:10.774647image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:11.791246image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:12.680508image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:13.643798image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:14.529402image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:15.524401image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:16.512667image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:17.383012image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:18.367114image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:19.272052image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:20.250134image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:21.233321image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:22.091666image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:23.043774image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:23.928802image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:07.892555image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:08.921261image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:09.793527image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:10.823652image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:11.837519image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:12.725288image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:13.690867image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:14.576129image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:15.573229image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:16.560568image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:17.429766image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:18.415023image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:19.319862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:20.297688image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:21.278015image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:22.136242image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:23.089144image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:24.071862image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:07.940444image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:08.968450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:09.843100image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:10.871859image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:11.885265image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:12.770045image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:13.737363image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:14.624022image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:15.622075image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:16.607936image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:17.477406image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:18.463267image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:19.367420image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:20.347480image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:21.322233image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:22.182450image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:23.135437image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:24.116872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:07.987872image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:09.016200image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:09.892295image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:10.921788image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:11.935320image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:12.815807image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:13.786889image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:14.777706image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:15.670686image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:16.658510image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:17.525969image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:18.514244image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:19.512498image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:20.400608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:21.369024image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:22.230442image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:23.183117image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:24.164497image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2023-01-06T12:09:08.035000image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
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Correlations

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Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2023-01-06T12:09:27.435250image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2023-01-06T12:09:27.550944image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2023-01-06T12:09:27.665463image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

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A simple visualization of nullity by column.
2023-01-06T12:09:24.765845image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

populationGDP_constant_2010_USDland_area_km_sqpopulation_%GDP_%land_area_%EF.EFM.OVRL.XDEF.EFM.RANK.XD1.1_ACCESS.ELECTRICITY.TOT1.2_ACCESS.ELECTRICITY.RURALSH.UHC.NOP1.CGSH.UHC.NOP2.TOSH.UHC.OOPC.25.ZSCC.ESTGE.ESTPV.ESTRQ.ESTVA.EST
040403958.01.924910e+1111095000.0066290.0038450.0086270.5363966.095.20000083.8000000.00053469500.01.439833-0.406263-0.403076-1.6089610.091515-0.438141
127460603.05.289283e+104463000.0047360.0012070.0034050.4268770.061.31544928.8289330.00053469500.01.439833-0.106927-0.104411-0.209070-0.102563-0.416847
216791425.01.666350e+1126997000.0023590.0023840.0204580.2194788.099.87473399.7540910.0000000.00.092441-0.921266-0.524908-0.408455-0.390580-1.176721
323225000.01.687492e+104254000.0040050.0003850.0032450.2003779.099.60000099.3000000.00053469500.01.439833-1.128821-1.200643-0.543732-1.776773-1.600370
427302800.03.353506e+104254000.0040440.0005220.0032920.1449897.099.90295499.8476400.00053469500.01.439833-1.062857-0.885467-1.207106-1.363840-2.100143
51756817.01.618431e+102576700.0002470.0002320.0019530.00000144.089.30000044.9000000.00053469500.01.439833-0.666252-0.9614420.293291-0.532352-0.863607
662958021.02.177120e+115108900.0103290.0043480.0039732.2107019.082.10000087.0000000.00053469500.01.439833-0.2301740.1774850.4549590.4847940.467803
724650400.02.004580e+104254000.0040440.0004000.0033080.2369885.099.63301899.4872420.00053469500.01.439833-1.080415-1.019983-1.301950-2.242906-1.833387
810886668.04.493364e+101553600.0015300.0006430.0011771.1904041.099.50000098.6414960.00053469500.01.439833-0.033658-0.043860-0.721317-0.242597-0.171652
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